Progressive Dispatching for Smart Distribution Grids
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Solution Overview
Problem
Current power distribution networks face reliability issues and voltage instability due to the integration of distributed power sources and dynamic loads, such as electric vehicle charging, with existing dispatching methods relying heavily on experience and lacking intelligent data-driven optimization.
Innovation Solution
A progressive multi-time scale dispatching method that coordinates distributed power sources, micro-grids, energy storage devices, and controllable loads using a four-phase system: long-term optimization for network development, mid-long term for periodic variations, short-term for energy balance, and ultra-short-term for emergency management, optimizing power supply and reducing energy losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed power sources and dynamic loads are integrated into the distribution network, then power supply flexibility and energy efficiency are improved, but system reliability deteriorates due to frequent operating status changes and voltage instability
Solution Approach 1:
The dispatching method segments the distribution network into multiple controllable zones or regions, allowing independent optimization and control of each segment. This segmentation enables the system to maintain stability in unaffected areas while accommodating distributed power sources and dynamic loads in specific zones, thus resolving the conflict between flexibility and reliability.
Solution Approach 2:
The invention implements dynamic dispatching strategies that adapt to real-time operating conditions. The system continuously monitors and adjusts dispatching decisions based on the status of distributed power sources and dynamic loads, enabling the network to maintain reliability while accommodating changing conditions through real-time optimization.
2Ease of operation
If traditional dispatching methods relying on experience are used, then implementation simplicity is maintained, but dispatching intelligence and optimization capability are insufficient
Solution Approach 1:
The dispatching method incorporates feedback mechanisms that collect real-time data from the distribution network, analyze operating conditions, and automatically adjust dispatching decisions. This feedback-driven approach enables intelligent optimization while maintaining ease of operation through automated decision-making processes that learn from historical data and real-time measurements.
Solution Approach 2:
The system implements self-service capabilities where the dispatching algorithm automatically optimizes power flow allocation, load balancing, and resource coordination without requiring manual intervention. The intelligent agent autonomously makes dispatching decisions based on predefined objectives and real-time conditions, enhancing dispatching intelligence while keeping the system easy to operate.
3Productivity
If multi-time scale progressive optimization is implemented, then resource allocation optimization is improved, but system complexity increases due to multiple coordination phases
Solution Approach 1:
The multi-time scale optimization is segmented into distinct time horizons (short-term, medium-term, long-term), with each scale addressing specific optimization objectives. This segmentation allows the system to tackle complex resource allocation problems in manageable stages, improving productivity while controlling complexity by focusing on different aspects at different time scales.
Solution Approach 2:
The dispatching system implements periodic optimization cycles at different time scales, where short-term adjustments are made frequently, medium-term optimizations occur periodically, and long-term planning is updated less frequently. This periodic action structure enables comprehensive resource allocation optimization while managing system complexity through structured, repeating optimization patterns.
Data Source
AI summary
smart power distribution system and a method to progressively dispatch the power is described. The method steps are all automatic and self-adaptive. The method can be executed in an unattended manner to automatically correlate real time data vs. historical data, planning data vs. operation data. Based on a long cycle periodical variation and short-term random variations in load, and taking into account the temporary load power supply and maintenance needs, a multi-stage progressive multiple time scales optimal dispatching method is developed, including the distributed power, micro-grids, energy storage devices, electric vehicles charge-discharge facility and other elements of the Intelligent power distribution systems, to achieve coordinated operation of the network, power, load resources to ensure a continuous safe and reliable smart power distribution system operated at high quality and efficiency.